Attitudes of older patients toward artificial intelligence in decision-making in healthcare
Artificial intelligence (AI) is rapidly transforming healthcare, promising improved diagnostics and efficiency. However, the ethical implications and patient perspectives, particularly among older adults, remain underexplored. This study aimed to explore the ethical considerations and attitudes of older patients toward AI in healthcare decision-making. We conducted qualitative research in the geriatric wards of three hospitals in Dhaka, Bangladesh, using the Technology Acceptance Model and a phenomenological approach as the guiding frameworks. Semi-structured interviews were conducted with 21 purposively sampled participants, all aged 60 and above. Data were collected in Bangla, transcribed, translated into English, and analyzed through thematic analysis. Methodological rigor was maintained through member checking, triangulation, and reflexive practices. Five key themes emerged: (1) Trust and skepticism toward AI’s decision-making capabilities, with concerns about its ability to address nuanced health needs; (2) A strong preference for human interaction over technological efficiency, highlighting the irreplaceable value of empathy; (3) Ethical concerns regarding informed consent, emphasizing the need for transparent and comprehensible AI integration; (4) Apprehension about privacy and data security, reflecting a trust deficit in AI’s handling of sensitive health information; and (5) Mixed perceptions on AI’s role in enhancing or diminishing the quality of care. This study underscores the need for transparent, patient-centered AI systems that enhance, rather than replace, human elements in care. Addressing ethical concerns about privacy, autonomy, and informed consent is critical to fostering trust and acceptance among older patients.
Authors
- Md. Abdun Noor
- Mohammad Salafsha Afrin
- Moustaq Karim Khan Rony (ORCID: https://orcid.org/0000-0002-6905-0554)
- Mst. Rina Parvin (ORCID: https://orcid.org/0000-0003-0111-6163)
- Mst. Amena Khatun
- Fazila Akter (ORCID: https://orcid.org/0009-0009-5887-8370)
- Tuli Rani Deb
- Latifun Nesa
- Most. Tahmina Khatun
- Mosammat Ruma Akter (ORCID: https://orcid.org/0009-0001-7502-9233)
- Jeni Begum
- Hasina Begum Most
Institutions
- Hiroshima University (JP)
- Shahjalal University of Science and Technology (BD)
- Rajshahi Medical College (BD)
- Leading University (BD)
- Western Norway University of Applied Sciences (NO)
- Daffodil International University (BD)
- Bangladesh Medical University (BD)
- Pundra University of Science and Technology (BD)
- Islamic University (BD)
- International University of Business Agriculture and Technology (BD)
- Armed Forces Medical College (BD)
- National Institute of Nuclear Medicine & Allied Sciences (BD)
Publication Details
- Journal
- Journal of Medicine Surgery and Public Health
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1016/j.glmedi.2025.100193
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 2.62